Tang Lingli

Satellite Visualization Tool Based On the ArcGIS Engine and OpenGL

The satellite resource visualization tool provides a large and clear view about the moving and working status of the satellite, it is an important part of the satellite mission operation system. ArcGIS Engine is a collection of GIS components that can be embedded, allow the user to add 2D and 3D world map to the applications. The way of using ArcGIS Engine for satellite resource visualization was discussed in this article, and since it doesn’t provide enough methods for visualizing details, a way of extending ArcGIS Engine by OpenGL is also be discussed. The architecture of the visualization tool built with ArcGIS Engine and OpenGL was presented, which was used in the HJ-1A/B, SJ-9, ZY-3, ZY-01-C ground systems.

A temperature and emissivity retrieval algorithm based on atmospheric absorption feature from hyperspectral thermal infrared data

Land surface temperature and emissivity separation (TES) is a key problem in thermal infrared (TIR) remote sensing. However, because of the ill-posed problem, the retrieval accuracy still needs to be improved. Through exploring the offset characteristics of atmospheric downward radiance, a temperature and emissivity retrieval algorithm based on atmospheric absorption feature is proposed from hyperspectral thermal infrared data. Furthermore, an optimal channel selection is carried out to improve the efficiency and accuracy of method. The simulated results show that modeling errors less than 0.4K for temperature and 1.5% for relative emissivity for contrast materials and the accuracy is similar to the ISSTES method (Borel, 2008) for high emissivity materials. Furthermore, the proposed method can enhance the retrieval accuracy for low emissivity materials, that is approximately temperature 0.5 K and emissivity 2.1%.

Evaluation of temperature and emissivity separation method using the hyperspectral data for contrast emissivity surfaces

Land surface temperature and emissivity separation (TES) is a key parameter in the physical processes of land surface energy and water balance at regional and global scales. Various methods have been proposed to retrieve the temperature and emissivity for the high emissivity (close to 1) materials. This work addressed the iterative spectrally smooth temperature-emissivity separation method (ISSTES) proposed by Borel (1998) for retrieval of temperature and emissivity from the simulated hyperspectral thermal infrared (TIR) data for contrast (high- and low-) emissivity materials. The results show that small modeling errors less than 0.3 K for temperature and 0.01 for contrast materials are shown in ISSTES algorithm. A sensitivity analysis is carried out and the experimental results show that the instrumental noise, the atmospheric downwelling radiance and the atmospheric transmittance have a great influence on the retrieval accuracy, especially for low-emissivity materials.

The Autonomic Model in Remote Sensing Data Processing System

The remote sensing data processing system is a typical distributed computing system, which concerns the parallel computing, spatial database, image processing and a series of computer technology. With remote sensing technology and the level of the continuous development, its data processing systems are becoming increasingly complex, management and maintenance costs continue to rise. Autonomic computing systems can effectively reduce system complexity, lower maintenance costs. Based on the characteristics of remote sensing data processing system, with the concept of autonomic computing, the knowledge model and mathematical model using in different scenarios are described in this paper.